Pre-surgery urine metabolomics may predict late neurodevelopmental outcome in children with congenital heart disease. Issue 10 (October 2019)

• Record Type: Journal Article
• Authors: Vedovelli, Luca; Cogo, Paola; Cainelli, Elisa; Suppiej, Agnese; Padalino, Massimo; Tassini, Maria; Simonato, Manuela; Stellin, Giovanni; Carnielli, Virgilio P.; Buonocore, Giuseppe; Longini, Mariangela
• Is Part Of: Heliyon. Volume 5:Issue 10(2019)
• Publication Date: 2019-10
• Subjects: Cardiology -- Medicine -- Neuroscience -- Pediatrics -- Physiology -- Biological psychiatry -- Metabolomics -- Congenital heart diseases; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2019.e02547
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Background: From fetal life until cardiac surgery, complex congenital heart diseases (CHD) exhibit different hemodynamic and oxygenation patterns that can lead to alteration of the metabolic profile. We used a metabolomic approach to identify urine metabolic markers before cardiac surgery, aiming to define the physiology of patients with complex CHD and to contribute to predict their neurodevelopmental outcome. Methods: In a prospective, observational, single-center study we enrolled 28 patients with complex biventricular and univentricular CHD aged less than 5 years, on stable hemodynamic conditions, and with no genetic anomalies. We analyzed urine samples, collected at the induction of anesthesia, by 1H NMR spectroscopy. Profiles of 1H NMR spectra were submitted to unsupervised (principal component) and supervised (partial least squares-discriminant) multivariate analysis. Neurodevelopment was assessed by neuropsychological and adaptive functioning testing. Results: Principal components analysis divided CHD patients metabolic profiles in two distinct clusters (RED and BLACK). Metabolic profiles belonging to the RED cluster showed higher levels of accumulation of citric acid cycle intermediates and glucose compared to the profiles in the BLACK cluster, indicating a possible switching to anaerobic metabolism. Patients belonging to the RED cluster were significantly more prone to show an adverse neurodevelopment pattern (p = 0.01). Conclusions: The application of metabolomic analysis to CHD children permitted a deeper insight on their metabolic status that could help to obtain a better understanding of the physiological implications and to predict long-term neurodevelopmental outcome.